5 4 3 2 1 zero. All engine running.

Liftoff. We have a liftoff.

>> That's one small step for man, one giant

lift for mankind.

>> The world's largest airliner. Each wing

is big enough to hold five tennis

courts. This new technology made it

possible to meet the user's crucial

needs. Enter the computer and a new age.

>> What a computer is to me is it's the

most remarkable tool that we've ever

come up with. And it's the equivalent of

a bicycle for our minds.

>> Here I am playing a game of chess with a

computer which is analyzing board

positions and and applying a certain

kind of intelligence to figure out what

its next move should be. That's the

subject of our program today, artificial

intelligence.

[Music]

The good future of AI is one of immense

prosperity where there is an age of

abundance. Everyone can have whatever

they want.

>> We're still in the very early innings of

AI.

>> I would say the rate of progress is

exponential right now.

>> Every time I think that we are

overstating the impact of artificial

intelligence, something comes along that

tells me we aren't making enough of it

on the show. You know, there's no

60-minute clock on this thing. This is

an infinite game.

>> Think about solving a problem that would

take humans thousands of years to solve.

Those who can harness and govern the

things that are technologically superior

will win and it will drive economic

vibrancy and military supremacy.

>> The Trump administration believes that

AI will have countless revolutionary

applications. We believe that America's

destiny is to dominate every industry

and be the first in every technology

and that includes being the world's

number one superpower in artificial

intelligence.

>> It feels like every tech revolution of

our lifetime has been leading to this

moment.

[Music]

[Applause]

[Music]

>> See, you're going there.

All right, everybody. Welcome to winning

the AI race. Uh, this is our first event

in DC.

>> Can I can I get permission from our

leader to sit down?

>> Yes, you're here. You may sit. You may

sit.

Thanks for coming out, everybody. And,

uh, we put this event together in just a

couple of weeks uh, in order to have a

really important discussion, winning the

AI race. This is something America has

to do and it's something we will do and

we're going to do it through the way

we've won every other technological race

through grit, entrepreneurship,

uh, and dogged competition. The

difference with this administration is

they're actually engaging with the

technology industry. And, um, today

we're bringing together many members uh,

or all members of the administration

here to talk about it. And none of this

would have been possible uh without our

bestie David Saxs deciding that he would

take um some time and become our zar of

crypto and AI. And I would like to just

start with a huge round of applause for

David Saxs.

David, you've been here for

six months.

>> I'm sorry, but did you actually prepare?

This is excellent so far.

>> No, I'm just speaking for

>> No, it's excellent.

Keep going. Keep going. He wants to be

invited back to DC.

>> They told me I've got 12 hours left on

the ground.

>> I think that White House tour is going

to happen after all.

>> It might just happen. Might just happen

in the air.

>> But in all seriousness, u you've been

here for 6 months and we all know how

capable you are. Uh but my lord, uh this

administration is on a heater when it

comes to crypto and AI. I am absolutely

and I think I speak for everybody in our

industry thankful and wildly impressed

but not surprised at the pace at which

you've led crypto and AI. What's the

first six months been like for you?

>> It's really been incredible. I mean I

never expected to go into government at

all and uh really as a result of

President Trump coming on our podcast uh

a year or so ago. Uh that began uh a

relationship that you know eventually

led to uh me being offered this this job

and I took it because I just thought it

was a once in a lifetime opportunity to

work for a president who really wants to

get things done for the American people

and you can see that he that just every

day he works so hard uh to to push

forward his agenda for the American

people and I think uh AI and crypto have

just been two of those issues. Uh but

it's been a lot of fun to work on these

things because we are getting a lot

done.

>> Yeah. last week and this date, this date

today, you put we put together in just

like the last 10 days as an opportunity

to talk about your

>> action plan that was getting the

president's action plan getting

published today. But we we should invite

Jacob out because Jacob partnered with

us from Hillen Valley from Jacob

Hellberg. Uh come on out and join us.

>> Yep. Our our new fifth bestie. Yeah,

Jacob Hellberg.

>> There you go.

>> Nice to see you, brother.

>> Good to see you. and um

>> good to see you.

>> Good to see you.

>> Freeberg, your team and Jacobs

>> uh put a ton of work into this and

we have a lineup that is just absolutely

outstanding. So, thank you Jacob for the

for the hard work from your team and

Freeberg the hard work from your team to

put this all together. Maybe you could

give everybody an idea of the questions

we want to address today and what the

format's going to be.

>> Absolutely. So um the Hill and Valley

Forum is a community of technology of

builders and policy makers who believe

that technology is an engine of wealth

creation and is an indispensable pillar

for American national security. And it

was incredibly exciting to have the

opportunity to engage in this event

which is actually going to cover um a

lot of the topics that everyone in our

community cares about. Um, ultimately we

believe that the and I actually said

this in my confirmation hearing not too

long ago that uh we're at an inflection

point. We are in an AI race and uh so

the different parts of the programming

today will be a series of conversations

that will cover the different facets of

how technology will actually create

wealth for our country. I think is like

very important as we talked about who do

we want to have on stage and how do we

want to talk about the uh the

president's action plan that David

shared with us was to highlight that

there are new industries being created

because of AI industries that couldn't

have existed a decade ago and so we've

got a couple of those examples and then

there are these industries that are

enabling and accelerating AI and that's

mining energy chips fabs and data

centers so we've got conversations

across each of those that's kind of this

enabling conversation and then

fortunately uh we've been able to get a

lot of folks from the administration to

join us today to talk about the

government's role

>> in enabling this economic transformation

that's already underway and I just want

to say one point I think what what's

become apparent to me and I think is

wrong in the press narrative today is

that AI is destroying jobs I think what

we are seeing on the ground is an

incredible job creation engine that's

underway and so I think it's very

important to highlight that and share

that those stories because they're not

told enough and I think there's a real

opportunity to kind of bring them to

light and that's hopefully what we can

kind of get through today.

>> And Chimath just uh coming around the

horn here. Doesn't matter if you're a

Democrat, Republican, independent,

moderate, this issue transcends party.

Uh this is the issue of our lifetime and

there's a lot of hard questions and a

lot of hard debate. maybe you could just

speak to this administration's ability

to bring in a lot of disperate opinions

uh and work together across the aisle

and with all members of the industry.

>> I mean, look, I think historically

you've had a fork in the road where you

can view technology as either optimistic

and glass half full or pessimistic and

glass half empty. The optimistic glass

half full view says that the country

that can harness AI or any of these

leading critical edge technologies is

able to garner most of the gains and

then those economic gains can be spread.

Then it's a debate about how to spread

those gains within a country and within

economy and then from there with

economic supremacy you also have

military dominance and now you're a

superpower and you remain strong. The

problem is that historically we've gone

in the other direction where there has

been this mistrust and in that mistrust

you've had global competitors emerge and

create I think real fundamental

existential risk for our place as a

superpower.

>> Well said.

>> So the great thing over these last you

know frankly

six months has been a massive pivot back

into this idea that America is the best.

We should not be ashamed of the things

that we've created and these incredible

technologies and these incredible people

should be celebrated.

>> Yeah.

>> And let's go and win the race.

>> Let's win. Okay.

>> And by the way, how we're going to win

the race.

>> The action plan. Uh Sax, I know you

invited Michael Katios to join us here

today. Director of the Office of Science

and Technology Policy. Should we have

Michael come out?

>> Yes, Michael come out.

>> Yeah.

>> Michael, come on out.

>> Please welcome Michael.

[Applause]

>> All right.

How are you guys? see it.

>> Oh, thank you. So,

>> so I'll I'll kick this off. Um,

>> so President Trump in his first week in

office uh uh signed an executive order

that directed us to create this action

plan. Uh Michael and myself and and the

national security adviser and the

objective was to figure out how the US

would dominate in AI. um from his first

week in office, President Trump has made

this a priority and we see it we do see

it as this global competition or global

race and the consequences of losing that

race would just be unthinkable because

AI is going to have such huge

ramifications for our economy and also

for our national security. So, the

United States has to win it. And um

working with Michael and the Office of

OSTP, we we put out a plan today that

has 90 concrete actions that at least

the executive branch can take to help us

win the AI race. And I want to call on

Michael in just one second, but I'm just

going to outline the three big pillars

of the plan. So, number one is

innovation. There's just no substitute

for innovation. You have to out innovate

your global competition. You can't

regulate your weight just to winning the

the uh the AI race. So number one is we

we have a lot of things in the plan that

are going to help our private sector,

our startups or our um tech community uh

in out innovate the competition. Number

two is infrastructure. We have to have

more and better AI infrastructure, data

centers, energy, manufacturing in the

United States. And number three is the

AI ecosystem. We want to have the the

biggest ecosystem. We know from Silicon

Valley that the companies that create

the biggest ecosystems are the ones that

win. You have the most developers on

your platform. you have the most apps in

your app store. Those are the companies

that create, you know, uh those are the

companies that dominate industries. In a

similar way, the United States has to

dominate by creating the the AI stack

for the entire world. So, those are the

three big pillars of this plan. Let me

call on Michael. Can you I guess tell us

how you know I guess maybe speak to the

process of how this plan was created the

last 6 months. I know your office did a

ton of work on this and then I guess if

you want flesh out some more of the

important details as you see it.

>> Yeah, absolutely. Once the president

signed the executive order assigning us

this task, the first thing we did was

actually an issue issue an RFI, very

exciting government activity. And we

asked the country, hey, what should we

include in this plan? And I think to be

honest, I think we're all surprised with

what came back. We had over 10,000

responses come from all corners of the

country. We had Hollywood actors sending

us responses. We obviously had tech

companies. We had everyone you can

imagine. And I think it really showed

how impactful this particular technology

is to everyone in every industry in the

US. So we ingested a lot of those

comments went out to all the uh agencies

that work with and and in some ways

touch AI and came together with this

with this plan. Now if you think about

it um you know there's been a lot of

national strategies that companies put

that countries put out there over the

last five or six years and what we

really wanted to focus on um is in the

title itself an action plan. We wanted

things that we could accomplish in the

next 6 to9 months to accelerate and

ensure that we can win this race. So if

you think about the first pillar which

David talked about which was the

innovation pillar, what's really key

about innovation is we want the next

great AI discoveries to continue to

happen here in United States. We have to

create an environment that allows that

to happen. And when we talk about

deregulation, the way I like to think

about it is, you know, you can't create,

there's never really going to be a law

that says, hey, this is how we regulate

AI. What ultimately is going to happen

is these AI technologies are going to be

built into so many other technologies.

Whether it's drones flying self-driving

cars, whether it's FDA approved AI

powered medical diagnostics, all these

different agencies are going to be

touching technologies that are powered

by AI. And it is incumbent on us to

create a regulatory environment where

these technologies can thrive and not be

hindered by the government. The next

piece of innovation I think is really

key is using the power of and the data

that the government has to drive

scientific discovery through artificial

intelligence. You know we have seen in

this first wave of AI great great

progress in the way that LLMs are able

to handle coding for example but we can

do so much more than that. There's

incredible data sets department of

energy has for example at their national

labs that can help power a lot of next

generation discoveries and things in

material science in medicine and that's

what this AI plan calls for and and

drives. Um the next pillar which is

about infrastructure,

people talk about this all the time and

it's it's about how do you create a

regulatory environment that encourages

and actually accelerates the ability of

our power generators and our chip

builders to be able to do what they need

to do here in the United States. We can

uh plan calls for categorical exclusions

for AI related activities which can

allow data centers and other power

generation to happen on federal lands.

And that's going to be coupled with all

sorts of other efforts to really

accelerate the velocity that we can

build power and ultimately run these

data centers. So let's talk about before

we run out of time one of the most

important issues which is the talent

wars.

>> Yeah, we um going to stay focused on AI

here. We'll leave the border and

deportations off the table, but we'll

talk about something super important

which is recruiting talent from around

the world.

>> This administration, we've gotten

different signals and obviously it's a

very controversial issue here in the

United States. What do we have to do in

terms of immigration and let's just call

it recruitment because that's really

what it is recruiting the best and

brightest from around the world to come

work on our team as opposed to say team

China.

>> What do we have to do? What is the

administration's philosophy on

recruiting the world's best AI talent

>> in the action plan? I think what we

bring to light and I don't think it's

talked about enough is to power and

successfully drive continued American

leadership in this domain. It is not

simply about having the greatest a

engineers but is also having all the

other parts of the workforce which needs

to drive this forward. You know we

talked to some companies like Caruso and

others who are building these large

infrastructure builds around the US. The

challenge that they're facing is in

electricians and HVAC talent and the and

the AI plan itself spends a lot of time

and energy directing um various agencies

whether it's department of labor and

others who have these reskilling and and

programs to sort of train these people

up to be able to fill that void. So for

us it's about attracting here to the US

the greatest scientists and engineers

but it's also to be able to train the

American workforce to be able to do the

necessary jobs put that forward.

>> Michael um what's the philosophy going

forward on the thing you mentioned just

before this which is there's these

enormously valuable data sets that sit

inside the DOE that sit inside of FDA

where presumably if we made them

available to private industry

particularly American private industry

the gains could be incredible. Is that

an open-source philosophy? Is that a

licensing philosophy? How do you think

it should best serve the American

economy to get this stuff out there?

>> Generally, the government has taken an

open- source approach to this. And the

general challenge that we've seen over

the years is there's been a lot of lip

service to, hey, let's unlock data for

the American people. And the main

challenge is, and for all of us who are

in AI, the format of that data itself

actually matters a lot. if it's like

dirty, nasty data that isn't homogenized

in any way, it's not particularly

helpful. And I think that's going to be

a big effort that the DOE is going to

Department of Energy is going to try to

do to make this better and possible. And

what was great in the uh recent

legislation that was passed in BBB was

and actually $150 million ticket to the

Department of Energy to build an AI for

science program that very much is going

to be working on this exact problem.

Should there be federal preeemption on

AI regulatory um schemes? So, there's

been a conversation about doing this to

ensure I think right now there's over a

thousand state laws that have been

proposed or passed that have some

regulatory effect on AI and tech related

technology. Should the federal

government preempt all of that and raise

it up?

>> I think generally preeemption is an

issue that comes up very often broadly

in technology. You have this issue with

uh privacy for many years. What we're

trying to face today and what we talk

about in the plan itself are actions

that the executive branch can take

itself. And a lot of preeemption

discussion revolves around what Congress

can or can't do. So we don't necessarily

lean hard on that because we focus on

things we can accomplish.

>> Right.

>> And just to just to add to that, so it's

true the the action plan doesn't speak

to that issue um Freeberg very much. But

I I do think there is a real threat to

national security that's brewing by

virtue of the fact that like you said,

we've got a thousand bills going through

state legislatores right now, all

regulating AI in different ways. If this

continues, we're going to have a

patchwork of 50 different state

regulatory regimes as opposed to one

seamless national network. And look,

China is they've declared that AI is a

national priority for them. It's they

understand how strategic it is. And I

think if we hobble our AI innovation

with a patchwork of 50 different state

regimes, I think it's going to hurt us.

So I don't, you know, we weren't ready

to declare a policy yet in the action

plan, but I think it's something that's

going to have to be looked at uh over

the next year or so.

>> Thanks for thanks for joining us,

Michael. Everyone, the director of the

Office of Science and Technology at the

White House. Well done.

>> Thanks.

>> Great job. Pleasure.

Thank you everyone to the besties in the

Hill Valley Forum for the warm welcome.

Uh I'm Chris Power, the founder and CEO

of Hadrien and I'm here to talk to you

today about our company. The mission is

to re-industrialize America. We do this

by building AI powered factories in the

United States.

So you might ask why is this important

and why should you care about

manufacturing in the United States?

Well, what I realized before I came to

this country is that we're in a global

race.

So every great nation gets built by

having the best industrial power first.

That gives you the best military,

usually the navy. Then you end up with

the reserve currency after a conflict

and you kind of rule the free world in

what we've called Pax Americana.

Like all great companies, you kind of

get lazy through that success and you

end up offshoring all your heavy

industrials to the developing country

and then when a conflict comes around,

you're kind of in real trouble because

you offshored the thing that gave you

the power in the first place, which is

heavy industry.

The last three times this happened, it

was a pretty good trade for the West. It

went from the Dutch, the British, the

American Empire when we won World War

II. This time around, in this kind of

two decade period where we're fighting

the AI race, the climate, settling the

stars, it's really the United States

versus the CCP.

And bear in mind that we won World War

II not because we had a defense

industrial base necessarily, but because

we were the industrial powerhouse of the

world. And when there was a time of

crisis, we had all our commercial

manufacturing companies pivot to defense

when we really needed them the most. And

you had, you know, watch makers making

warship navigation equipment. Ford was

switched from building cars to building

bombers. And it was because of this

industrial power, you know, our tanks

weren't so great. We just had tons of

them. This is how we won.

Unfortunately, since the 1970s through

to the 2020s, we've basically hollowed

out the middle of America and offshored

every bit of manufacturing we possibly

can. This started with Nixitting opening

up China, let them into the WTO. They

were the world's factory. This is like a

huge strategic mistake and it's

completely hollowed out good jobs in

America as well as left us in a very

strategically dangerous position in

terms of our industrial power.

So, while China de-industrialized us,

they industrialized themselves and they

treated manufacturing not as economics

but a national security priority. And

now we're in this 20-year window where

staring down the threat of Taiwan, we're

in real trouble. So just how far behind

China are we? Well, in munitions, China

has automated factories that can produce

a thousand a year, whereas we run out of

missiles in the first seven days of any

war game conflict. And then we can't

reproduce that ammunition for like 3

years. In ship building, they're 200

times greater than us. We produced a

grand total of five ships last year.

Pharmaceuticals are all offshore.

Drones, iPhones, we don't make any of

them. And bear in mind in

pharmaceuticals the CCP makes all our

antibiotics. This is why

industrialization is so important. And

more importantly, this gets back to the

AI race for talent is while the US is

still the global powerhouse in software

and AI talent, we made China into the

global powerhouse for manufacturing

talent. And what we realized through

building this company is that while US

defense manufacturing, which was all we

have left because we offshored

everything else, is really important

because we let all those jobs go, the

entire base is basically a bunch of

patriotic Americans that still know how

to do skilled trades that are in their

60s are retiring faster and faster and

faster. So the underpinnings of our

entire defense industrial base is this

American talent that knows how to do the

job, but the rest of the country forgot

how to manufacture.

This is a screenshot of one of China's

munitions factories. You can Google this

online. And it's a myth that it's just

lowcost labor in China anymore. They are

very advanced at production. Whereas in

the United States, underpinning all our

defense primes and our industrial base,

we basically have skilled Americans that

are retiring faster and faster and

faster, supporting 100200 billion

industries across all these different

ways to bend, cut ship metal that you

need to then put it into drones, ships,

satellites, rockets.

So while China is racing ahead of us,

we're really falling far behind and we

forgot how to manufacture. So what we

realized was we have to build full stack

AI powered factories to solve this

problem. Secondly, the number one issue

is this massive skilled talent shortage.

Remember if you look at ship building or

any of these other industries, we are

begging for millions and millions of

welders or machinists that we you could

give me a billion dollars and we can't

hire them in this country anymore

because we lost that skill. the

production not having inventory is real

deterrence and that you've got to do

this by re-industrializing the country

to create more jobs not replace them or

automate away and that it's always about

national security not economics so we

set out to solve this problem by

building automated factories driven by

AI in the US

3 years ago when we started this journey

we figured out how are we going to do

this well the answer was just start

running a factory and build all the AI

software at the same time which was a

hilariously painful journey in the early

days of the company this is what factory

1 looked like We partnered with some of

America's greatest aerospace companies

to really beta test this for a good 18

months. What can we automate? What can't

we?

This is one of our first tiny parts that

we ship to America's greatest rocket

provider. And now we're up to the point

where we're building whole products.

We built Opus, which is a full stack

platform for AI autonomy of factories

that does a couple of really important

things.

In 2024, we launched Factory 2, once out

of this beta phase, scaled 10x in a

single year with the fastest growing

manufacturer in the country, and now

lucky enough to support America's

greatest companies, uh, both startups,

defense primes.

And this is what the most advanced

factory, in our opinion, looks like in

America today. This is our scaled

factory 2 in LA.

Here you see cutting metal coming from

raw material,

shaving this down into micron precision

tolerance components that go on rockets,

satellites, jets, and drones. And what

you see as you go through this is in

legacy industry, in a de-industrialized

nation, you've got really skilled people

on every machine. Hadrien's advanced

factories look and operate more like a

data center.

We're really proud of having pulled this

off, but the journey is not over yet

because again, this is a whole of nation

100 to200 billion problem.

So where do we actually get to and what

sort of productivity gains can you get

in AI and manufacturing and are we

creating more jobs? So firstly, most

factories in the US run at only a 20%

uptime. It's not really that productive.

We have a four times jump in

manufacturing productivity.

Secondly, and more importantly, we have

a 10x jump in workforce productivity.

And again, because we have a such a

scarcity of skilled talent in this

country, you actually need that AI

powered jump to even create the capacity

in this nation to be able to build

ships, drones, and rockets.

The second important thing is speed to

get people in these jobs. So, if you're

an advanced manufacturer, it can take

you up to a decade to get really good at

what you do. Whereas at Hri, we've

managed to make it so that we can train

anyone in 30 days. And most importantly,

100% of our workforce are from

non-factory backgrounds. They've never

set foot inside a factory before. These

are folks straight out of high school.

They're retired from the military. They

had a desk job. They were a nurse or bus

driver from, you know, 18 up to 40. And

this is the most important thing that

people have got to realize about the

power of advanced AI in manufacturing is

that we need this productivity boost to

just be able to compete with China and

catch up on these skill trades that we

lost. And this is the most important

thing that AI is doing for us is

enabling huge, huge workforce growth.

So, where are we at? You know, we've

been on this journey. In 2025, we're

going multicategory, multiactory, and

I'll show you our new factory that's

launching AI powered in six months in

the great state of Arizona, as well as

launching a dedicated gigafactory. And

you can think about this as like

everyone in defense and aerospace needs

a Tesla Model 3 factory.

This is our beautiful uh new facility.

It's about four times the size of the

one in LA, launching by Christmas. We

signed a lease a couple of weeks ago.

It'll be online in 6 months. And the

most important thing is we'll be

creating 350 plus new AI powered jobs in

scarce talent industries where America

just needs this leverage to get ahead.

The other thing if you listen to the

secretary of the Navy and

re-industrialize what is the number one

problem in ship building, submarine base

and munitions, it's actually that

there's millions and millions of jobs

that we need to fill because we don't

have skilled trades anymore. We don't

have the volume of the people. So we

need this productivity boost. So in

2026, we're launching advanced factories

targeted at America's greatest

production challenges, submarines,

ships, and munitions.

So by the end of this year, we'll have

three facilities up and running. Our

headquarters, Factory 2 and Factory 3 in

LA. But as we re-industrialize the

country powered by AI, like where is

this really going to get us to? Well, to

solve this problem for the country and

fulfill the mission, we need to have

factories in every state. And you got to

remember that AI in manufacturing is

creating thousands of jobs because we

offshored everything. And we need this

productivity boost to give our nation

the capacity it needs. Reshore all these

jobs, pull them back into the middle of

the country, and make sure that we're

creating millions and millions of jobs

along the way. Thank you for having me.

It was a pleasure to be here.

>> Chris, I think we wanted to kick this

off. We have a couple minutes to just um

cover what you've introduced which is I

think like a really important

opportunity. China has roughly 3 million

factories. US has 250,000. The

assumption is they've got cheap labor.

Looks like they've got automation.

Things are very different on the ground

than what folks read about as we try and

compete. What industries are going to be

first from a manufacturing perspective

that we can actually compete

successfully and do we need trade

tariffs in order to succeed on the

competitive landscape? So I think

there's two really important points. One

is there's industries that we have to

reshore. Specifically in defense, we

have to produce submarines and ships and

munitions. Uh we have to produce things

like rare earth magnets and drones. We

just have to do it. Um the tariffs

really help. And this trade policy is

really important because you got to

understand that yes, China is more

competitive than us, but the CCP also

nationally subsidizes the cost of

energy, the cost of raw material. And

because we've kind of degraded this

capacity like not having nuclear in the

US like we can't compete on those raw

inputs. So it'll start with our most

critical industries first. But I think

as AI goes through manufacturing you'll

create millions of jobs and that will

allow us to reshore more commercial

volume not just in defense. And I think

that's the most important thing.

>> And you've talked about this like

degrading you know sort of

infrastructure and what that means in

terms of workforce but then how

reshoring also requires this like

upskilling. I know you guys have this

like associate named Owen that you guys

took I think like literally straight out

of Home Depot. Can you like give us a

little bit of his story and just like

what that represents in terms of you

guys upskilling labor?

>> It's it's really incredible. So, as I

said in the presentation, 100% of our

people never set foot inside a factory

before. And I think we really didn't do

a great job as a nation by convincing

everyone they needed a four-year college

degree to have a really good job. And

we've hired people that, you know,

packing shelves at Home Depot. Now

they're running 10 machines at once. And

actually, what we are seeing is that

most of those people when they're

exposed to software or AI, they're very

smart. And we've promoted a lot of those

people into leadership management or

software engineering roles. And I think

re-industrialization with AI is about

creating new jobs, but also reattaching

people to the Silicon Valley economy and

not just having it on the coast and the

cities.

H

>> how um are you going to compete with

people having gig labor and making 30 40

bucks an hour being a door dasher? Um

and we have the lowest unemployment in

our lifetimes, 4% right now. Is it

realistic to find all this labor out

there or do we have to have some people

immigrate to this country in order to

fill those jobs?

>> For for us specifically in defense, um

we we can't we have no choice in

immigration because it's a regulated

environment. So we have to upskill

Americans.

>> Secondly, what we see maybe not in LA or

the coastal cities, but across the

country, there's lots of

underemployment. Some of our favorite

people have desk jobs where they're a

parallegal and they were filling out

forms and they hate it and they want to

come in factories and work on the

national mission. And I think for us

it's a lot about getting people

inspired. And then secondly, with this

level of productivity jump, we can

actually afford to give people

incredibly good healthcare and

incredibly good pay. And I think a lot

of Americans want to go back to work in

a real environment that's for the

national mission.

>> You uh you showed some incredible images

and video of these very intricate

machines. are do you make the machines

that then make all the machines or is

there a supply chain risk as

>> there is a huge supply chain risk so we

actually invented via the air force a

lot of these advanced machines um and we

forgot how to make them so the main

sources of supply are actually our

allies in you know China is number one

we don't buy for them because they've

got cyber security holes all over the

place Germany South Korea Japan the

insight that we had was they're actually

just really dumb computers and software

and AI can actually upscale and

overpower them um and really have a

lead. But it is a huge supply chain risk

of not building the machines that build

the machines in the country anymore.

>> Right. To economically compete though,

do you I I was trying to parse if you

were asking for the government to give

you support since the Chinese government

is underwriting their companies with

free energy. Are you explicitly asking

the government to help with say paying

for reskills training or um maybe in

some way deferring your energy cost or

can you make this economically work? We

make it economically work because in the

US there are really two markets. There's

the stuff that has to be onshore for

defense and aerospace and then there's

this offshore market that's 10 times

larger. Um you know commercial aircraft,

a lot of that is in China. For us we can

compete in the US because we've got to

create all these new advanced jobs

because we just don't have the skills

anymore. If we want to reshore the

commercial volume that is not regulated

to be onshore, we have to do tariffs and

economic policy because it's not an even

playing field. It is right now companies

versus the CCP.

>> What would that look like in in terms of

execution? You would want uh them to

pick up the retraining, the energy cost,

part of their salaries.

>> It's it's really three things. It's the

cost of energy. It's the cost of raw

materials, aluminum, steel. 90% of the

cost of that is actually energy. And if

we level that playing field, then we can

go compete in what we're great at, which

is the, you know, American software and

the American spirit and AI powered

workforce.

>> So the silver bullet is energy.

>> Yeah. And then tell us about the actual

software. Do you have a team that's

writing a lot of control systems and or

AI models themselves or you're taking

things that are off the shelf and you're

fine-tuning them? How are you how are

you doing it?

>> Unfortunately, because American

manufacturing software is 30 years

behind Silicon Valley, we had to build

everything ourselves from scheduling

systems to the deep tech. And the key

insight that we had is the faster we

grow, the more data we are labeling,

right? So we always do things 80%

automated with a human in the loop. And

as we label this complex manufacturing

data, you know, this is where our AI

models actually kick in because

manufacturing has been offline for 30

years. So there is no stack overflow.

There's no GitHub code base to train a

model on. We had to train it ourselves

off our own label data as our experts

were ticking and tying the automation. I

mean histo like traditional automation

is purpose-built, does one thing. A lot

of engineering goes into making it do

that one thing really well. Are you

leveraging things like to Chimat's

question vision action models that allow

you more extensibility with one

particular piece of machinery and like

or when does that start to happen from a

tech perspective in your view?

>> Right. Right from the start. So the way

oddly that customers translate data to

their supply chain is by giving them

20page PDFs full of hieroglyphics. So we

actually have to train huge vision

models on interpreting that. What does

that mean? It's it's very complicated

and it usually takes an expert 50 hours

to pour over that. So, it's vision

models, it's training engines on the

data, it's also training engines on

reinforcement learning of, hey, we we

made a part with automation. Was it high

quality or not? Did it actually work?

And embedding all of these in the

workflow real time is is the magic trick

here with AI.

>> And you're not doing this stuff just

like you know on the coast, right? Your

next factory is sort of more middle

America. Like how do you end up choosing

where to put that?

>> The most important reason why we

selected Arizona was because of

permitting, energy, and regulations. You

know, we got to go fast, right? We got

to build this in 6 months and then we

will expand into the middle of the

country kind of left to right on the map

and I think that's the most important

thing is we're going to be able to

expand into all these cities and states

where the manufacturing jobs were

destroyed and we're going to bring him

back.

>> Are you guys investors?

>> Oh yeah, just led the uh series C which

we just announced last week and uh join

the board much to uh Chrisin.

>> You're on his board?

>> I'm on his board. Yeah. Is that

terrifying?

>> It is very terrifying.

>> How long have you guys known each other?

>> Uh too long. Yeah, too long. Yeah, I was

board observer for a while and uh you

know tried to avoid getting the official

seat.

>> Uh you got a date for a while and then

now we got married.

>> Well, Chris, thanks for being here

today.

>> Thanks for hosting me, guys. Pleasure.

>> Yeah, thanks for the education.

>> Thanks, man.

>> There is a huge fire going on right now

at Philadelphia Energy Solutions. Oh my

gosh. Again, look at this guys. Look at

this video right now. Today, the Navy

remains a formidable fighting force, but

even officers within the service have

questioned its readiness.

>> Developing right now, gushing for hours

with no end in sight. Thousands of

barrels of crude oil spilling from a

tank. Uh the report does an estimate of

what the need is to bring the overall

grade up to a B, which is what the

society sort of determines to be

adequate, and it's like $4.59

trillion.

Heat

[Applause]

up

here.

[Music]

A company that started in my college

dorm is now a company that manages over

500,000 of the world's most critical

pieces of infrastructure.

Now, at Gecko, we build robots and AI

models to help unlock physical the

physical world.

Now you see we build when we rebuild

robots we wanted to build them that

could fly, swim, crawl and walk on any

surface to gather the most amazing

information and data sets that have been

forgotten about the physical data

layers. Now all those data layers are

incredibly valuable when you're able to

unlock and use AI models to drive

incredible and important outcomes.

Now, I started the company in the energy

sector, deploying the technology to help

prevent catastrophic failures in

downtown power plants. Now, we've to

expand into mining, metals, and

manufacturing as well as for the

defense. And so, we're helping to deter

conflict by getting ships out of dry

dock on time and not uh and patrolling

this the the borders. Also, we are

helping the air force ensure that planes

are in the air and not in hangers. And

then just last week when the president

was in Pittsburgh, my hometown, we just

signed an amazing deal that ensures that

we can help revitalize manufacturing in

the United States again by helping to

build ships and subs.

Now, the energy sector has been

incredible and we in a lot of other

sectors as well. But what I begun to

realize is that the most impactful thing

that Gecko Robotics can do to help

ensure that we uh deter conflict and our

most impactful for national security is

actually in the energy sector. You see,

President Trump is absolutely right and

his executive order today calls out an

extremely important um extremely

important reality that the companies

that can unlock energy are going to be

the ones that can dominate in the AI

race.

However, as you can see from the graph

here, China is on pace by 2030 to 3x the

amount of generation by against the US.

But this isn't the whole story. You see,

we constantly think about AI as an

energy consumer. However, I'm here to

tell you that artificial intelligence

can actually be used and unlocking

energy production in ways that you never

seen before.

Now, inputs really matter to being able

to unlock this potential.

And CEO after CEO that I talked to in

the energy, mining, manufacturing, and

defense sectors will tell you that we're

trying to figure out how to unlock

artificial intelligence to supercharge

everything. However, the value is just

not there. And it's no wonder the

consistent common factor between each

one of these sectors is Joe. Joe is out

there gathering information by hand,

trying to diagnose and get physical data

to drive really impactful decisions. But

it's important to understand that

Silicon Valley uh Silicon Valley

artificial intelligence researchers and

software engineers, they can't do much

with data sets coming off of the backs

of Joe and Joe's been armed with the

same technology for the past century.

So, it's no wonder that impact isn't

being unlocked in these sectors. And

unfortunately for Joe, it's a very

dangerous job as well. And someone dying

doing this job was actually one of the

things that inspired me to build Gecko.

We have to give Joe better tools in the

new century.

So, what I'm going to walk you through

right now is an example of exactly how

we do that for the power sector. We send

in robots. Robots that are gathering

information and data sets about the

physical environment. In this case, a

natural gas power plant. We're

understanding what the physical

environment looks like. And then we send

in other robots like this dog over here.

Now the robot dog is gathering

operational data sets to help

supercharge cantaliever our AI powered

platform where all the data sets are

coming into. You see we sell an

operations platform and data sets

gathered in the physical world is what's

enabling that.

We also send in wall climbing robots and

you can see the wall climbing robots to

your left and to your right. Now these

robots are going into the physical

environments and gathering health data

all while the app while the uh power

plant is actually online. Now the health

data is really important because we have

to understand process health data to be

able to optimize and feed into AI

models. But again this data set just

never existed before. So we had to go

out and actually get it physically in

the real world.

So robots like this supercharge our

ability to be able to drive models to

create largest amount of efficiency

gains. So this power plant for example

is supposed to be operating at 620

megawatts but it's not reaching its

capacity. It's only operating at 580. So

how do you unlock that? Well, when you

have all this information and data sets

that we've captured with robots, plus

all the data sets that customers have,

you're actually able to drive

optimization to see how to impact

efficiency and production. And so what

this AI model is doing is looking at the

data sets from the robot dogs as well as

data sets from the health the health

data from the robots to pinpoint that

there's actually a steam issue going

into the turbine. Now, an ability to fix

these things has actually been able to

unlock for this site and for many others

that we work on a 1% improvement to

efficiency. And this is just the first

place that we looked.

Now, it's also important to understand

that the assets that power the grid are

failing at a really fast rate. Now, this

power plant had assets like this tank

that was decaying at an at incredible

rates. It was supposed to be reaching

retirement pretty soon. But we were able

to determine predictively how to extend

the useful life of this asset by 10, 20,

and 30 years from all this data set.

It's really important to understand when

you culminate all the kinds of impacts

that you can have from this this kind of

technology, you get things like this

efficiency gains on the dozens of power

plants that we've been able to work at.

If you extrapolate that across the

thermal fleet in the US, that'll give

you 11.9 gawatt of new power without

putting a shovel in the ground. The

energy is able to be unlocked using

artificial intelligence. It's really

important to understand the statement.

AI shouldn't just consume, it should

create energy. And that's what we're

showing here. And not to freak anybody

out, but the DOE just came out with a

study that showed we have about four

years left of useful life on the assets

that power our grid. Now in this trend

it means that a 100 there's going to be

a 100 times the amount of blackouts by

2030 if we don't reverse this trend. But

what we're able to show not just with

power plants but mining metal

manufacturing as well as defense assets

you can actually extend the useful life

of infrastructure in some cases by 30

years and on average it's been about 35.

This is extremely important in ensuring

that we're able to reverse that trend

and ensure that America is well

positioned to ensure that we lead on the

energy and the in the energy race to

enable and unlock artificial

intelligence. Now, let me summarize

this. We spent so much time and I think

JD Vance has done a great job at at

highlighting how much effort and how

much data set have been gathered to

power AI models in the digital world.

And it's what makes chat GPC so

addictive. But remember, the physical

world has been forgotten about. And our

robots are going in to the fog of war to

try and decipher and unlock massive

amounts of information and data sets

that gives America and our allies unfair

advantages. Unfair advantages to unlock

things that we didn't even realize were

there.

And if you build software with an

ontology based on first principles,

gathering the data and building software

up from there, you're actually able to

deliver impactful things for Joe,

turning Joe into a PhD scientist or

engineer instead of forgetting about him

like a lot of Silicon Valley companies

have in the past. Unlocking potential

physical intelligence data drives um it

drives artificial intelligence and

that's how you're going to win the AI

race. Thank you.

[Music]

>> My name is Laura Deerdinas and I'm a

registered nurse here at Tampa General

Hospital. The last 17 years I've been

able to serve the neurointensive care

unit where we care for the most

vulnerable and critical care patients.

So before utilizing AI, it would take

hours to gather information, looking in

chart reviews, uh talking to nurses,

talking to physicians. We relied on

paper, pencil, um a lot of paper stapled

together with sometimes outdated data by

the time I was done going through uh 32

patients. and this is how we would try

and give reports. Bringing in AI, it has

significantly

changed the culture on the unit. I had a

charge nurse who never gave um a

multid-disciplinary round or report out.

She came on board. She said, "This is an

amazing tool. Look at this. It has all

my information already gathered and

collected." and she was able to report

out on the patients. It was completely

user friendly. She's like, "Laura, what

is this?" It is creating excitement

throughout the nursing community. Using

AI has provided more time to be with you

or your loved one at the bedside where

nurses should be. We are the heart of

healthcare.

>> Matt Troutman. I'm the vice president,

general manager for PRL industries,

supplier of components for nuclear

submarines at our service men and women

lives depend on. We are a fully

integrated foundry pouring metal all the

way through finished machine components.

2 months ago, we weren't getting after

any of the problems on the shop floor.

engineering director told me all his

team was doing was quoting a 3-day

process to quote apart paper files, old

archives, data tables, emails, side

communications, all this which ends up

getting lost in the fray. Now, using an

AI tool, they are getting halfway

through that process in minutes frees

them up to get back out in the floor and

do what an engineer does best, which is

solve problems to provide the Navy with

the best quality products in the

shortest amount of time. And this is

what AI is going to help us do.

Understanding part location and status

that is a gamecher. We can now talk very

clearly with the customer if that part

is now to become the primary focus of

the business cuz it's a critically

needed part for a ship construction. You

get notified. It's an automatic

notification. We can see the exact

status. Here is the impact. How can we

be better? How can we do more? And this

is how we're answering that call. With

AI, we match the speed of the quality

management process to the speed of the

workforce and the machine capabilities

and we will truly see a multi-step

change in the amount of product that can

come out of any company in this supply

chain.

More jobs for American workers here at

Pro.

>> My name is Julie Nordberg. I'm a

registered nurse leader here at UP

Health System Marquette in the heart of

Michigan's upper peninsula and we are

really the only game in town as we like

to say it. The next closest hospital to

us that could service us is downstate

which is about a 4-hour drive. Prior to

using AI, it took a lot of time to go

through the patients charts to see where

they need to be. It took a lot of time

just to try to communicate with people.

I think that's a fear that everybody has

is that AI is going to replace people.

But AI in the way it's being used here

could never replace our frontline staff.

You know, that the vibe is I think it's

just one of excitement that everybody's

just proud to be part of this and to say

that we're doing it here and we're

honing it in and tweaking it and and

using it to enhance our care and using

it to help our staff. Having this kind

of communication hub and facility

snapshot has helped has helped

everybody. For the nursing staff, I

think being able to see everything in

one spot has just revolutionized kind of

how they are able to provide care.

I don't think anybody is sad to get rid

of a meeting.

The impact on patients is earlier

detection, which means earlier

treatment, um, which is a better

outcome, life-saving for some of them.

That's where I think this is going to

help us a lot is because we we don't

have as much manpower as those big

academic centers. So having the AI in

the background doing some of that leg

work for us is is huge.

I joined PCNA in 2018. We have built

over 11 billion batteries in the last 8

years. I walked out onto their massive

production floor for the first time. I

knew right then and there I wanted to

make this technology accessible for

anyone who wanted to learn it.

people coming from the tourism industry

and the hospitality industry. Quite a

few technicians that have fixed slot

machines in a past life. People from

automotive companies, people who are

used to repairing cars, however, have

never seen equipment, you know, at this

scale and with this complexity. You

know, we don't really um have to pick

and choose what people's backgrounds are

because we do have this very powerful

learning tool that makes it easy for

anyone to be able to enter this

industry.

It is taking our historical maintenance

records, pairing it with our machine

data, and is now starting to understand

early warning signs of a breakdown, and

deploy our technicians to equipment

before it ever actually breaks. This

helps minimize our production losses,

keep our technicians safer. We're taking

reactive events, turning them into

predictive events.

We used to honestly lose a lot of

technicians because they would lose

their confidence, think, "Hey, maybe

this isn't for me." I pulled the

supervisor off the floor and said, "Hey,

you got to come listen to this idea and

you have to help us make it better

because you're the one who lives it

every day." And they immediately started

suggesting new features. They were

telling us what was wrong with the old

systems and we were coming up with

solutions on the spot. So, this is

really helping people feel like they

belong here. We don't believe AI should

replace human talent. We believe it

should elevate it. Our workers are very

excited. They have a tool that they can

turn to to help them learn at their own

pace. It really puts the power back into

their hands.

[Music]

All

right, Christian's joined us from Hillen

Valley and 137.

And Sean, welcome.

>> Thank you. Great to be here.

>> Christian, you want to kick us off?

>> Yeah. Uh, thanks for having me. It's

It's nice to be here. I'm definitely uh

feel for the first time like a guesty of

the besties. Um,

>> don't it up. And, uh, yeah. Uh,

this is great. So, Sean, thanks for

coming. We were talking a little bit

earlier. maybe this is a great place to

start. Um, obviously we have the good

fortune to be investors in Paler for 15

years. We've seen the growth of the

company, but particularly lately you've

been pushing this messaging. I think

it's been incredibly exciting of how AI

is not a force for job destruction. It's

a force for job creation. It's also a

way that you can give superpowers to the

average American worker. And obviously,

we've seen a little bit of content here

and how it's already doing that today.

>> I want to start by saying many of the

workers in the video are actually here

today joining. Uh Laura, the nurse from

Tampa General, actually brought her

12-year-old daughter. So, I think the

ultimate litmus test is not just how

excited are the American worker to

leverage AI, but how excited are they

for their children to exist in an

America that's really embraced AI. And

Julie has four kids, and she would tell

you how how much this has not only

transformed her view of her job, but the

view of her children's future. Uh I I

think the the right frame here really is

how do we give the American worker

superpowers? You know, we should not be

aspiring to build things that make them

50% more efficient. They're really 50

times more productive and to use that as

our our asymmetry in the competition

here. You know, our strengths are not

only AI, which is clearly an American

phenomenon, but also the ingenuity of

the American worker. And if you spend

time on the factory floor on the front

line, you see a very different narrative

emerging where you see people are

actually excited about these tools.

Every single one of those workers to a

tea said AI is giving them more time to

do what they do best to spend time with

the patient delivering care to actually

build the parts as an engineer to solve

the problems not to be caught up in all

the coordination and the paperwork

that's around these things. Uh that's

the future we should be unleashing.

>> Can you generalize the adoption curve?

What is it about a particular industry

or use case that makes it an early

adopter versus mid versus late that

you're seeing because now that you're

touching all these different industries.

Uh you probably have a good point of

view on this.

>> Yeah, my my take is actually a different

dimension of slicing that which is where

do the where does the institution

liberate their worker to drive the

adoption versus where are they trying to

forcefitit some sort of solution top

down. You know, AI is a method of

unleashing the agency of the worker, the

creativity of the individual, and

they're the ones coming up with these

use cases. I mean, Chris was talking

about it from Hrien where you'd be

surprised at how people with deep

mechanical intuition, traditionally

considered bluecollar workers, are the

ones who are able to pick up the skills,

build the applications, innovate on

their own processes, and have that

spread through the organization. Mhm.

And are you seeing that you have to

build vertical tools or generalized

tools for some horizontal kind of set of

users somewhere in the organization?

>> Well, I think that the opportunity with

AI is really that you can unleash what's

different about your business than all

the others. So there there's a degree to

which you can have uh generalized

solutions, but there's a lot of alpha to

be captured by understanding what's

unique about how we do things. How do we

lever up human taste? Everyone is afraid

of AI replacing the human. That's not

what I'm seeing. I'm seeing it it make

the the most the the person with the

greatest taste more valuable and an

ability to spread that to the breath of

the organization.

>> Let's talk about um something beyond

taste which is also like knowledge and

skill and tell us about AI inside of

healthcare. Um I think that a lot of

people probably think that we have an

incredibly cutting edge system of tools

and software that helps doctors and

nurses actually provision great care.

What's the actual reality that you guys

are seeing?

>> Well, sadly, I think with the forced

adoption of EHRs, what we saw is roughly

a havinging in the productivity of how

many patients you can see per hour.

>> A having

>> a having. Yes. So, we became half as

productive. And we really need to, you

know, the opportunity is to work

backwards from what is the care that

needs to be delivered? How do we build

the tools around that? How do we help

the the the nurses, the care staff spend

more time with the patients and less

time with the computer? And do you guys

see a world where in order to facilitate

that end market versus a different end

market you have a ensemble of many many

many different techniques and approaches

in AI or do you think it all sort of

gets form fit into this one trillion

parameter huge ginormous thing that kind

of tries to do everything?

>> I think the cardality of agents and

models is very high. I think there will

always be alpha to be achieved you know

improved differentiation improved

outcomes by specializing to the use

case. Now, it's great to start with the

general models, but you will specialize

over time.

>> And do you feel pressure to do that now

or do you think that'll just be a

natural evolution over time as

>> Yeah, I think it's a journey that people

kind of get on like you realize like,

wow, look how much better things have

gotten with this. Now, how do I go get

the next incremental piece of

performance out of it?

>> You know, I'm just having this thought

as we sit here and discuss this. If you

think about any experience we have in

service that has a long wait time where

we feel like we got more time with the

practitioner, it's the perfect place for

AI to create more abundance and

healthcare and education are the two

that that come to mind uh where people

could just offload their chores and the

people who are getting the service can

use AI to maybe start the conversation

on second base or third base. What other

industries are you seeing after

healthcare education where AI can have

that dramatic of an effect where the six

week wait time to see a doctor the three

or four other students who are getting

tutored are ahead of you and maybe you

don't need as much help so you don't you

never get the tutoring. The

>> the place I'm most excited about it is

really in reindustrialization. So it

because there's so much dwell time in

the value chains around

>> what does that mean dwell time in

industrial

>> where you're just waiting for someone

else to figure out how to approve

something or the coordination costs mean

that it's essentially dead weight loss.

>> Give an example there. Yeah.

>> Uh you saw it with the the submarine uh

industrialbased partners there where

they're working on quoting a part to the

Navy. That means you have to go gather

all of this data. You have to look at

historical archives. All of that is

time. You're not making a part or

solving problems. that's just it's just

sitting there. The factory floor is

idle,

>> right? So, how do we get rid of that

dwell time so that you can be utilizing

the the capex that you actually have to

the maximum extent possible and then if

you start if you zoom out that's like

one part manufacturer. You're you exist

in a massively complicated supply chain

and you just end up with all these busy

weights along the way here.

>> Yeah. That's so profound. A friend of

mine said who's in that industry, you're

only as efficient as your worst

supplier.

>> Exactly. Yeah. And a second part of that

which uh the Panasonic energy example

really touched on is how do we train our

workers? You know, so here you have

exquisite Japanese technology. It used

to take three years to train a worker on

it. Now with an AI assistant, the

workers who are prior casino workers,

you know, they're not not from this

industry are able to get up the curve in

three months. So you think about how we

can use that to more quickly absorb the

slack that's happening as we as we adopt

AI and and democratize opportunities. So

much so I have so much conviction as

we've launched the American Tech Fellows

program at Palunteer to find blueco

collar workers at our customers in in

the heartland overlooked folks who have

a natural proclivity.

>> How do you find them? How do you find

them?

>> Well, some of them

>> beyond just saying apply like

>> yeah some of them are at our current

customers. The idea really came from us

organically where it's like wow who is

building the most compelling

applications? It's the guy on the

factory floor, not a formally

credentialed computer scientist, mostly

an autodidact, but there's immense not

only grit, but ambition. They have the

drive to reshape their own organization,

to reshape the processes. Let's bet on

that person. going earlier, does that

mean, and I'll ask the same question

many times today, that college

education, the traditional four-year

liberal arts degree, doesn't matter as

much, that kids can go from high school

or earlier in their careers into a new

workforce and get well trained and well

suited to to make money and and succeed

in life?

>> Yes. I I think I think the traditional

college degree is is dead and uh we

should be betting on the American

worker.

>> Well, on that point, can you talk about

the tech fellowship? uh I got to

recently see a bunch of demos from the

first cohort with you and is really

incredible what you guys are doing

there. Maybe give a little bit there and

then maybe also talk about the

opportunity for other companies to

follow this trade school framework as we

uh end here.

>> Yeah, I mean it's really kind of an

elite trade school. So like finding

people with me mechanical intuition who

have done things, some of them are right

out of college, some of them are 20

years of experience, but they're really

>> your first trade school that you guys

have done, right?

>> Yeah, that's right. Uh and we have just

enormous demand from our customers.

We're like, who are people who have

these skills, you know, and it's it's

not classically trained, college

educated people. They don't have these

skills actually. So, the market's not

meeting and they don't know how to

source these folks. So, I can credential

them. I can put them through the boot

camp in four weeks and place them with

my customers to go unleash AI within

their organizations.

>> It's incredible.

>> Sean, thank you, Sean. Thank you.

>> Thank you so much.

>> Well done.

>> Thanks, man.

>> Thank you.

>> That's great.

>> Thank you so much. Cheers. All right.

Next up, uh, we have Paul from Y

Combinator. Please welcome

>> Oh, Paul Buhight.

>> Blue Height.

>> Paul Buhight.

>> Paul Blue.

>> Paul, you created Gmail talking about

efficiency and making the world more

efficient.

>> And also, I believe

>> we work together.

>> You came up with the slogan.

>> We worked together, too.

>> The slogan don't be evil.

>> Yes.

>> Yeah.

>> Yeah.

>> How'd that turn out? I I don't know what

she It's an attempt at alignment, right?

Like we we worry about AI alignment.

What do you What do you tell the super

AI once you've built it?

>> Yeah. Um you're at Y Combinator now.

>> Although you recently said you're

stepping down, right? Or you're

>> uh partner ameritus. We're starting a

new firm uh Standard Capital. So Got it.

>> Oh, that's exciting.

>> Yeah.

>> Wow.

>> Let's talk about the game on the field

with startups. You get to see startups

in year zero and year one. Um, and one

of the primary

thesises I think we all have is vibe

coding and making coding not a

roadblock. I think Paul Graham's great

innovation at Y Combinator was saying

I'm just going to accept two or three

people who actually build the product.

In fact, in the YC application, it says

who wrote the code for this? Who's

writing the code? Just so you can make

sure that you're actually hiring coders.

What are you seeing on the field in

terms of vibe coding? Because people are

now

>> great question. the you know English is

the new programming language. It's only

two or three% of the country knows how

to code probably half that code well

enough to do a startup. So here we are

um could we be on the precipice of 10

times as many startups 100 times as many

startups?

>> Absolutely. I mean that's that's the

dream. Um that was actually you know YC

was started 20 years ago uh based on

PG's insight that actually it's getting

easier to start a startup right. It used

to be you had to have a big mountain of

money, you you know hire a big team,

etc. And his realization was you can

start a startup with just a couple of

people and um you know basically ramen

few few kids live living off of of

ramen. Um and that's proven to be true.

Uh and our belief is with AI that

actually just goes that much further,

right? Because the universe of people

who are able to create uh apps using

something like Replet uh is enormous.

And so, uh, my, I think maybe most

optimistic vision of what we're doing

with all the AI is essentially putting

all of these tools of wealth creation in

as many hands as possible.

>> Do you think that English is I think

it's Andre Carpathies that said this,

right? Like, do you think English is the

ultimate destination language that

everybody will use to code or do you

think it gets abstracted even further

beyond that where you sort of think

things and they just kind of appear? Uh

I think it might be a little while until

we can just think them. But clearly uh

that's the direction right is that you

have a a dialogue with the AI and so you

describe okay not quite like that more

like this. Um and the direction is

essentially just that it becomes easier

and easier for us to realize our visions

and for everyone to realize our visions

not just people who are

>> well let me ask you this question. You I

mean that clearly grows the funnel

right? So now we have 100 million, 500

million, a billion people, two billion

people, whoever can speak English can

now code. How does it, how do you think

about that as one of the best computer

scientists that America's ever created?

>> Mhm. How do I think about all those

people having the ability?

>> Yeah. I I mean, I think it's great,

right? Anything um you know, our

philosophy is that I don't want to see

all of the power concentrated in a small

number of large organizations. I think

that's bad for everyone. It's bad for

freedom. Um, and so what we want is to

give that power to as many people as

possible so that everyone can create um,

you know, apps and it might just be

something for their own local community.

It it's not not every one of those apps

is going to be the next Google

obviously, but the more people can

create wealth in their own community and

in their own lives, we spread the

prosperity everywhere. Are you seeing in

the applications you get to YC or that

you've heard of more physical AI,

robotics, automation, those sorts of

tooling? Because as this becomes easier,

it actually leads to the leap. Hey,

maybe I could do this as a robot and I

get a robot to do a particular thing and

that creates an opportunity for new

business. Has that become a big kind of

growth curve right now is physical AI

and

>> absolutely that number of robot arms at

the most recent demo day was was

striking. I think everyone is starting

to work on that and again as as the

things that used to be difficult get

easier um we just start doing more

difficult things but absolutely I think

and I think robotics

>> all technology curves

>> yeah exactly and and I think that's

going to open up you know whole new

realms that were previously impossible

or impractical so

>> so does that create new industries is

like I think a key

>> absolutely

>> point which is like what I think is most

under misunderstood about AI is it's not

about the displacement of doing old

things but it's about activating new

things that are complex and historically

not tractable, but now they're

tractable.

>> Right. Exactly. So, I mean, if you think

about just the fundamentals of wealth

creation, the inputs are essentially

energy and intelligence, and we're about

to unleash essentially an abundance of

intelligence where like the total global

intelligence is going to 10x, right? And

so, that will enable us to 10x our total

wealth. And that's going to come in a

lot of different forms like you know as

we start to have AI science labs for

example where the AI can actually start

running its own experiments producing

its own data um I think our

understanding of biology is going to be

incredible. You know in 20 years we'll

be able to know how a drug affects the

body without ever actually testing it.

And my prediction is actually our our AI

models will be more predictive than